EP3881288A1 - Automated motion correction in pet imaging - Google Patents
Automated motion correction in pet imagingInfo
- Publication number
- EP3881288A1 EP3881288A1 EP19755728.3A EP19755728A EP3881288A1 EP 3881288 A1 EP3881288 A1 EP 3881288A1 EP 19755728 A EP19755728 A EP 19755728A EP 3881288 A1 EP3881288 A1 EP 3881288A1
- Authority
- EP
- European Patent Office
- Prior art keywords
- imaging
- dimensional volumetric
- target tissue
- data
- image
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000003384 imaging method Methods 0.000 title claims abstract description 114
- 238000012937 correction Methods 0.000 title claims abstract description 35
- 238000000034 method Methods 0.000 claims abstract description 41
- 230000003068 static effect Effects 0.000 claims abstract description 37
- 210000000056 organ Anatomy 0.000 claims description 29
- 239000013598 vector Substances 0.000 claims description 17
- 238000002600 positron emission tomography Methods 0.000 claims description 13
- 230000002123 temporal effect Effects 0.000 claims description 10
- 210000001519 tissue Anatomy 0.000 description 23
- 230000000737 periodic effect Effects 0.000 description 19
- 238000012633 nuclear imaging Methods 0.000 description 10
- 238000012879 PET imaging Methods 0.000 description 6
- 230000007547 defect Effects 0.000 description 3
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- 238000003745 diagnosis Methods 0.000 description 3
- 238000002059 diagnostic imaging Methods 0.000 description 3
- 238000009792 diffusion process Methods 0.000 description 3
- 230000037406 food intake Effects 0.000 description 3
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- 238000010276 construction Methods 0.000 description 2
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- 206010011224 Cough Diseases 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000000747 cardiac effect Effects 0.000 description 1
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- 238000002405 diagnostic procedure Methods 0.000 description 1
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- 238000002603 single-photon emission computed tomography Methods 0.000 description 1
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Classifications
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T11/00—2D [Two Dimensional] image generation
- G06T11/003—Reconstruction from projections, e.g. tomography
- G06T11/005—Specific pre-processing for tomographic reconstruction, e.g. calibration, source positioning, rebinning, scatter correction, retrospective gating
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
- A61B6/02—Arrangements for diagnosis sequentially in different planes; Stereoscopic radiation diagnosis
- A61B6/03—Computed tomography [CT]
- A61B6/037—Emission tomography
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
- A61B6/52—Devices using data or image processing specially adapted for radiation diagnosis
- A61B6/5258—Devices using data or image processing specially adapted for radiation diagnosis involving detection or reduction of artifacts or noise
- A61B6/5264—Devices using data or image processing specially adapted for radiation diagnosis involving detection or reduction of artifacts or noise due to motion
- A61B6/527—Devices using data or image processing specially adapted for radiation diagnosis involving detection or reduction of artifacts or noise due to motion using data from a motion artifact sensor
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R33/00—Arrangements or instruments for measuring magnetic variables
- G01R33/20—Arrangements or instruments for measuring magnetic variables involving magnetic resonance
- G01R33/44—Arrangements or instruments for measuring magnetic variables involving magnetic resonance using nuclear magnetic resonance [NMR]
- G01R33/48—NMR imaging systems
- G01R33/4808—Multimodal MR, e.g. MR combined with positron emission tomography [PET], MR combined with ultrasound or MR combined with computed tomography [CT]
- G01R33/481—MR combined with positron emission tomography [PET] or single photon emission computed tomography [SPECT]
-
- G—PHYSICS
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10072—Tomographic images
- G06T2207/10104—Positron emission tomography [PET]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
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- G06T2211/00—Image generation
- G06T2211/40—Computed tomography
- G06T2211/412—Dynamic
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- G—PHYSICS
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2211/00—Image generation
- G06T2211/40—Computed tomography
- G06T2211/416—Exact reconstruction
Definitions
- aspects of the present disclosure relate in general to nuclear imaging systems, and more particularly to motion correction for nuclear imaging systems.
- Time-of-flight (TOF) nuclear imaging such as TOF positron emission tomography (PET) is used to construct two-dimensional and/or three-dimensional images of structures within a patient.
- TOF PET and other TOF nuclear imaging detects coincidence events representing near simultaneous detection of annihilation photon pairs using a pair of detectors.
- the TOF PET system determines the difference in time between the detection of the two photons (e.g., the time of flight) and localizes the point of origin of the annihilation event that occurred between the two detectors.
- PET imaging of individual organs can include at-rest scans and/or stress scans of the target organ.
- periodic and non-periodic motion of the organ can result in image blur or defects.
- Periodic motion includes recurring, expected motion of the organ, such as a heart-beat, respiratory motion, etc.
- non-periodic motion can result in unusable (or non diagnostic) images due to motion blur or changes in location.
- a method for automated motion correct of nuclear images includes receiving a first set of imaging data including a plurality if annihilation events detected during an imaging period and generating a plurality of four-dimensional volumetric images from the imaging data for the imaging period.
- Each four- dimensional volumetric image includes a target tissue.
- At least one motion correction is determined for each of the plurality of four-dimensional volumetric images.
- the at least one motion correction is determined using target tracking data generated for the target organ over a time period associated with the four-dimensional volumetric image.
- Corrected imaging data is generated from the first set of imaging data and the at least one motion correction and at least one static reconstruction image including the target tissue during the imaging period is generated from the corrected imaging data.
- a system in various embodiments, includes a first imaging modality configured to generate a first set of imaging data including a plurality of annihilation events during a first imaging period and a computer configured to receive the first set of imaging data and generate a plurality of four-dimensional volumetric images from the imaging data for the imaging period.
- Each four-dimensional volumetric image includes a target organ.
- the computer is further configured to determine a motion vector offset for each of the plurality of four-dimensional volumetric images. The motion vector offsets are determined using target tracking data generated for the target organ over a time period associated with the four dimensional volumetric image.
- the computer is configured to generate a corrected image data from the first set of imaging data and the motion vector offsets and generate at least one static reconstruction image including the target organ during the imaging period from the corrected imaging data.
- a non-transitory computer readable medium storing instructions.
- the instruction are configured to cause a computer system to execute the steps of receiving a first set of imaging data including a plurality if annihilation events detected during an imaging period and generating a plurality of four-dimensional volumetric images from the imaging data for the imaging period.
- Each four-dimensional volumetric image includes a target organ.
- the instructions are further configured to cause the computer to execute a step of determining a motion vector offset for each of the plurality of four-dimensional volumetric images.
- the motion vector offsets are determined using target tracking data generated for the target organ over a time period associated with the four-dimensional volumetric image.
- the instructions are further configured to cause the computer to execute the steps of generating corrected imaging data from the first set of imaging data and the motion vector offsets and generating at least one static reconstruction image including the target organ during the imaging period from the corrected imaging data.
- FIG. 1 illustrates a PET imaging system, in accordance with some embodiments.
- FIG. 2A illustrates a plurality of static images of an organ including non-periodic motion, in accordance with some embodiments.
- FIG. 2B illustrates a polar image generated from the plurality of static images of FIG. 2A, in accordance with some embodiments.
- FIG. 3 illustrates a method of motion correction for static images in a PET scanning system, in accordance with some embodiments.
- FIG. 4 illustrates a process flow of generating a plurality of four-dimensional volumetric images, in accordance with some embodiments.
- FIG. 5 illustrates target acquisition in a four-dimensional volumetric image of target tissue identified using a target acquisition process, in accordance with some embodiments.
- FIG. 6 is a chart illustrating motion vector offsets determined using target tracking of a target organ within a plurality of four-dimensional volumetric images, in accordance with some embodiments.
- FIG. 7 is a chart illustrating a plurality of sinogram plane shift correction values applied to image data during generation of corrected imaging data, in accordance with some embodiments.
- FIG. 8 A illustrates a plurality of reconstructed static images generated from imaging data collected during the imaging period illustrated in FIG. 6 using a traditional reconstruction process.
- FIG. 8B illustrates a plurality of reconstructed static images generated from the imaging data collected during the imaging period illustrated in FIG. 6, in accordance with some embodiments.
- FIG. 9A is a chart illustrating an imaging procedure having an imaging period including non-periodic movement of an organ, in accordance with some embodiments.
- FIG. 9B a plurality of reconstructed static images generated from imaging data collected during the imaging period illustrated in FIG. 9A using a traditional reconstruction process.
- FIG. 9C illustrates a plurality of reconstructed static images generated from the imaging data collected during the imaging period illustrated in FIG. 9A, in accordance with some embodiments.
- FIG. 10A illustrates a plurality of static images of an organ generated using a method of motion correction, in accordance with some embodiments.
- FIG. 10B illustrates a polar image generated from the plurality of static images of FIG. 10A, in accordance with some embodiments.
- a plurality of four-dimensional volumetric images are generated from imaging data for a predetermined imaging period.
- Each four-dimensional volumetric image includes target tissue.
- a motion vector offset is determined for each of the plurality of four dimensional volumetric images. The motion vector offsets are determined using target tracking data generated for the target tissue over a time period associated with the four-dimensional volumetric image.
- Corrected imaging data is generated from the first set of imaging data and the motion vector offsets and at least one static reconstruction image including the target tissue during the imaging period is generated from the corrected imaging data.
- FIG. 1 illustrates one embodiment of a nuclear imaging detector 100.
- the nuclear imaging detector 100 includes a scanner for at least a first modality 112 provided in a first gantry 116a.
- the first modality 112 includes a plurality of detectors 50 configured to detect an annihilation photon, gamma ray, and/or other nuclear imaging event.
- the first modality 112 is a PET detector.
- a patient 117 lies on a movable patient bed 118 that may be movable between a gantry.
- the nuclear imaging detector 100 includes a scanner for a second imaging modality 114 provided in a second gantry 116b.
- the second imaging modality 1 14 can be any suitable imaging modality, such as, for example, computerized tomography (CT), single-photon emission tomography (SPECT) and/or any other suitable imaging modality.
- CT computerized tomography
- SPECT single-photon emission tomography
- Scan data from the first modality 112 is stored at one or more computer databases 140 and processed by one or more computer processors 150 of a computer 130.
- the graphical depiction of computer 130 in FIG. 1 is provided by way of illustration only, and computer 130 may include one or more separate computing devices.
- the imaging data sets can be provided by the first modality 112 and/or may be provided as a separate data set, such as, for example, from a memory coupled to the computer 130.
- the computer 130 can include one or more processing electronics for processing a signal received from one of the plurality of detectors 50.
- FIG. 2A illustrates a plurality of static images 200a-200e of a target organ 202, such as a heart.
- the plurality of static images 200a-200e are generated for a predetermined imaging period, for example, using the nuclear imaging detector 100.
- a predetermined imaging period for example, using the nuclear imaging detector 100.
- movement, discomfort, and/or physiological reactions of the patient can result in non periodic movement within the data.
- significant artefacts and/or motion blur can occur.
- the plurality of static images 200a-200e include significant motion blur caused by the non-periodic motion of the patient during imaging. As shown in FIG.
- a polar image 204 of the target organ 202 generated from the plurality of static images 200a-200e also includes significant artefacts 206a-206b as a result of the non periodic motion.
- the non-periodic motion results in static images 200a-200e and a polar image 204 of a non-diagnostic quality, i.e., the images 200a-200e, 204 cannot be used for diagnosing defects or other issues in the target organ 202, resulting in the need to do additional diagnostic imaging of the patient and exposing the patient to additional radiation and discomfort.
- FIG. 3 is a flowchart 300 illustrating a method of non-periodic motion correction for PET images, in accordance with some embodiments.
- the method 300 is configured to identify and track the position of a target organ 204, such as a heart, during reconstruction of diagnostic images to allow removal and/or minimization of non-periodic movement and related artefacts.
- the method 300 allow generations of diagnostic images from image data that traditionally produces non-diagnostic images, such as, for example, the PET image data associated with the static images 200a- 200e in FIG. 2A.
- PET imaging data is received by a system, such as, for example, the computer 130.
- the imaging data can include PET image data for each detection event detected by an imaging modality, such as the first modality 112, during a nuclear imaging procedure.
- the imaging data is generated and provided to the system in real-time (e.g., immediately provided from the imaging modality to the system).
- the imaging data is generated by the imaging modality during an imaging period and is processed by the system during a later image generation period.
- the image data is provided in a listmode format, although it will be appreciated that the data can be provided in any format readable by the system and converted into a listmode format.
- the listmode data 402 includes a plurality of data points each including a first detector identifier (A), a second detector identifier (B), and time-of-flight (TOF), i.e., ⁇ (Ai, Bi, TOFi); (A 2 , B 2 , TOF 2 )... (A n, B n , TOF n ) ⁇ .
- the first detector identifier (A) and the second detector identifier (B) correspond to detectors 404a, 404b that each detect an annihilation event.
- the system uses the detector identifiers and the time-of-flight to identify a position 406, or voxel, for the annihilation event.
- the system generates static volumetric images including each annihilation event in the listmode data 402 over a predetermined diagnostic period, e.g., a four-dimensional volumetric images 408a, 408b (or frames).
- Each four-dimensional volumetric image 408a, 408b includes three spatial dimensions (x, y, z) and a temporal dimension (t) corresponding to the predetermined time period selected from the predetermined diagnostic period.
- the temporal dimension t includes 1 second incremental intervals, although it will be appreciated that shorter and/or longer temporal dimensions can be selected.
- a first four-dimensional volumetric image is generated for a first time period (e.g., 0-1 second)
- a second four-dimensional volumetric image is generated for a second time period (e.g., 1-2 seconds)
- an nth four-dimensional volumetric image is generated for an nth time period (e.g., (n-l)-n seconds).
- the total number of volumetric images generated is equal to the total imagine period (t totai ) divided by the temporal dimension increment t, e.g., 1 second, 2 seconds, 0.5 seconds, etc.
- the predetermined diagnostic period can include an entire imaging procedure and/or a portion of an imaging procedure excluding non-diagnostic imaging such as an ingestion and/or diffusion period prior to a tracer being distributed to target tissue.
- step 306 a dynamic image of the target tissue is generated for the
- a single continuous dynamic image is generated for the entire predetermined diagnostic period and/or a plurality of dynamic images for portions of the predetermined diagnostic period can be generated.
- the dynamic image is generated using imaging data generated by a second imaging modality 114, such as a CT imaging modality.
- the second set of imaging data is generated simultaneously with the set of PET imaging data.
- the position of a target tissue is identified within the dynamic image using one or more known target identification processes.
- the identification of the target tissue can include, but is not limited to, organ finding using a matched filter for acquisition and normalized cross-correlation for tracking.
- a center of the target tissue is identified within the dynamic image.
- a motion vector is generated for each four-dimensional volumetric image 408a, 408b using target tracking data generated from the dynamic image (or portion of the dynamic image) corresponding to the temporal dimension t of the selected four-dimensional volumetric image 408a, 408b.
- target tracking data generated from the dynamic image (or portion of the dynamic image) corresponding to the temporal dimension t of the selected four-dimensional volumetric image 408a, 408b.
- motion and position information from the dynamic image is used to identify the target tissue 410 and/or a center point 412 of the target tissue 410 within each four-dimensional volumetric image 408b, as shown in FIG. 5.
- any type of movement such as translational, rotational, skew, non-rigid transformations, etc. may be tracked and used to generate a motion vector.
- FIG. 6 is a chart 416 illustrating motion vector offsets 418 for the listmode data 402. The greater the offset 418, the greater the non-periodic movement of the target tissue 410 during the temporal period t of the corresponding four-dimensional volumetric image 408b.
- a non-diagnostic portion 422 of the listmode data 402 corresponding to ingestion and diffusion of a tracer molecule is ignored (e.g., not used for diagnostic imaging), although it will be appreciated that additional target tracking and/or diagnostic procedures may be performed that include the ingestion and/or diffusion periods.
- additional target tracking and/or diagnostic procedures may be performed that include the ingestion and/or diffusion periods.
- the signature of a target organ i.e., target tissue
- motion tracking through the changes in the target tissue can be tracked and motion correction applied according to the embodiments disclosed herein.
- corrected data including axial plane shifts (or other motion correction shifts) corresponding to the motion vector offsets 418 is generated for the listmode data 402.
- the plane shifts correspond to discrete shift values on a predetermined axis, such as a z-axis.
- FIG. 7 is a chart 450 illustrating a plurality of discrete shifts 452 applied to the listmode data 402 during generation of corrected data from the listmode data 402.
- a discrete shift value is applied to one or more voxels within the temporal period t to correct a position of the voxel during grouping and reconstruction.
- the corrected data is generated using only a predetermined diagnostic portion 420 of the imaging period.
- pre-processing of the listmode data 402 can be applied prior to generation of the corrected imaging data, such as, for example, correction for random coincidences, estimation and subtraction of scattered photons, detector dead-time correction, and/or detector-sensitivity correction.
- one or more reconstructed static images are generated from the corrected imaging data.
- the reconstruction can be generated according to known methods for generating PET diagnostic images from the corrected imaging data, such as, for example, filtered back projection, statistical-likelihood based-approaches (e.g., Shepp-Vargi construction), Bayesian constructions, and/or any other suitable method of generating static PET reconstruction images from the corrected imaging data.
- the method 300 results in the removal of artefacts, such as artefacts 206a-206b illustrated in FIG. 2B, and allows generation of diagnostic-quality reconstructed images from traditionally non-diagnostic listmode data 402.
- the listmode data 402 includes significant non-periodic motion, such as, for example, as highlighted by box 440 in FIG. 6.
- FIG. 8A illustrates a plurality of static images 502a-502c of the target tissue 510a generated from the listmode data 402 using traditional methods. As shown in FIG.
- FIG. 8A illustrates reconstructions of the target tissue 510b generated from the listmode data 402 using the method 100 of motion correction discussed in conjunction with FIGS. 3-7.
- FIG. 8B illustrates reconstructions of the target tissue 510b generated from the listmode data 402 using the method 100 of motion correction discussed in conjunction with FIGS. 3-7.
- the motion blur and artefacts of each static image 504a-504c has been eliminated and/or minimized as compared to the static images 502a-502c generated using a non-motion corrected data.
- the motion corrected static images 504a-504c are of diagnostic quality and can be used in patient diagnosis.
- FIG. 9A is a chart 516 illustrating motion vector offsets 518 for listmode PET data including non-periodic organ creep or movement during a diagnostic period 420a, for example, as highlighted by box 519.
- Organ creep occurs due to relaxation of one or more muscles during an imaging period. As the one or more muscles relax, the position of the organ within the patient shifts. This movement is non-periodic and results in distortion of a reconstructed image due to the change in position of the organ during imaging.
- FIG. 9B illustrates a plurality of static images 522a-522c of target tissue 520a generated by a traditional reconstruction from the listmode data associated with FIG. 9A using traditional methods. As shown in FIG.
- FIG. 9B illustrates a plurality of static images 524a-524c of the target tissue 520b generated from the listmode PET data of chart 516 according to the methods disclosed herein. As shown in FIG. 9C, the artefacts of the traditional static images 522a- 522c are removed, the edges of the target tissue 520b are more defined, and the diagnostic quality of the images 524a-524c is increased over a traditional static image 522a-522c.
- FIGS. 10A and 10B illustrate the scan data of FIGS. 2A and 2B, respectively, after undergoing a motion correction method as disclosed herein.
- the polar image 210 generated from the plurality of motion corrected static images 208a-208e does not contain any of the defects 206a-206b included in the original polar image 204.
- a diagnostic images 208a-208e, 210 can be generated from data that traditionally generated only non- diagnostic images.
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Abstract
Description
Claims
Applications Claiming Priority (2)
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US16/222,056 US11426131B2 (en) | 2018-12-17 | 2018-12-17 | Automated motion correction in PET imaging |
PCT/US2019/043855 WO2020131162A1 (en) | 2018-12-17 | 2019-07-29 | Automated motion correction in pet imaging |
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IL (1) | IL283980A (en) |
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EP3951701A1 (en) | 2020-08-06 | 2022-02-09 | Koninklijke Philips N.V. | Sharpness preserving respiratory motion compensation |
US11663758B2 (en) | 2021-01-07 | 2023-05-30 | Siemens Medical Solutions Usa, Inc. | Systems and methods for motion estimation in PET imaging using AI image reconstructions |
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US8150495B2 (en) * | 2003-08-11 | 2012-04-03 | Veran Medical Technologies, Inc. | Bodily sealants and methods and apparatus for image-guided delivery of same |
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CN101278318B (en) | 2005-10-05 | 2012-05-16 | 皇家飞利浦电子股份有限公司 | Method and system for PET image reconstruction using a surrogate image |
EP1991959B1 (en) * | 2006-02-28 | 2017-08-30 | Koninklijke Philips N.V. | Local motion compensation based on list mode data |
WO2008075265A1 (en) * | 2006-12-19 | 2008-06-26 | Koninklijke Philips Electronics N.V. | Motion correction in a pet/mri hybrid imaging system |
CA2632583C (en) * | 2007-05-29 | 2017-03-28 | Mcgill University | Deformable phantom apparatus |
JP5302326B2 (en) * | 2007-11-09 | 2013-10-02 | コーニンクレッカ フィリップス エレクトロニクス エヌ ヴィ | MR-PET periodic motion gating and correction |
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CN103381095A (en) * | 2012-05-03 | 2013-11-06 | 三星电子株式会社 | Apparatus and method for generating image in positron emission tomography |
US9451926B2 (en) * | 2012-05-09 | 2016-09-27 | University Of Washington Through Its Center For Commercialization | Respiratory motion correction with internal-external motion correlation, and associated systems and methods |
US11730430B2 (en) * | 2012-09-21 | 2023-08-22 | The General Hospital Corporation | System and method for single-scan rest-stress cardiac pet |
US9613441B2 (en) * | 2013-09-26 | 2017-04-04 | Koninklijke Philips N.V. | Joint reconstruction of electron density images |
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US10255684B2 (en) * | 2015-06-05 | 2019-04-09 | University Of Tennessee Research Foundation | Motion correction for PET medical imaging based on tracking of annihilation photons |
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